Method and a system for determining the maximum heart rate of a user in a freely performed physical exercise

ABSTRACT

A method and a system for determining the maximum heart rate, called HRmax of a user of in a freely performed physical exercise and using an apparatus with software and memory. An intensity model takes account of, in addition to heart rate, respiration rate and/or kinetics information.

RELATED APPLICATIONS

The present application is a continuation of, and claims prioritybenefit to, co-pending and commonly assigned U.S. non-provisional patentapplication entitled, “A METHOD AND A SYSTEM FOR DETERMINING THE MAXIMUMHEART RATE OF A USER OF IN A FREELY PERFORMED PHYSICAL EXERCISE,”application Ser. No. 16/691,781, filed Nov. 22, 2019, which claimsforeign priority to Finnish Patent Application No. 20186002, filed Nov.26, 2018. Each of the above-referenced applications are herebyincorporated by reference into the current application in theirentirety.

FIELD

This invention relates to a method and a system for determining themaximum heart rate, called HRmax of a user of in a freely performedphysical exercise and using an apparatus with software and memory means.

BACKGROUND

Prior art present plurality of methods based on regression analysis fordetermining maximum heart rate of user

Maximum heart rate represents the highest heart rate (HR) an individualcan achieve through physical effort, typically represented by a numberof beats per minute (bpm). The maximum heart rate value can be used tomake a variety of calculations, such as to determine a person's fitnesslevel, energy expenditure, or to create appropriate training heart ratezones an athlete should exercise at to accomplish a specific exercisegoal.

The most direct way to determine maximum heart rate is for a person toperform maximal exercise and measure their highest heart rate. In manycases, this is not an appropriate method as many people do not wish toperform such a strenuous test. For many others, such as seniors or thosewith medical conditions, performing such high-intensity tests may alsobe unsafe.

As an alternative, it is possible to estimate a person's maximum heartrate. There are many methods that currently exist to provide anestimation of maximum heart rate.

Simple calculations may use a person's age, gender, activity level, orweight. Most of these calculations are unreliable and may produce largeerrors. For example, age is known to correlate negatively with maximumheart rate, so age-based models of estimating maximum heart rate arecommon, the most well-known one being 220—[age]. However, these formulashave problems with accuracy and large outliers can occur. Thissignificantly reduces these formulas' usefulness.

Many methods are only able to update the maximal heart rate valueupwards, meaning that once a maximal heart value is found, it is onlyupdated if a higher one is detected.

However, it is a well-known fact that maximal heart rate decreases withage. Thus, if the same wearable device is used by the same user overmany years, it should be possible to update the maximal heart rate valuedownwards.

Because maximum heart rate is used in so many other physiologicalcalculations, inaccuracy of this value will have significant effect oncalculating anything else. For example, a 10-BPM error in maximum heartrate may increase the error of fitness level estimate by 50% (i.e., meanabsolute percentage error (MAPE) increases from 5% to 7.5%).

Thus, there is a need for a method of accurately estimating maximumheart rate that does not require a person to perform a maximum effortexercise. Therefore, there remains a continuous need for a more accurateand universal method to estimating maximum heart rate.

SUMMARY

The aim of this invention is to achieve a new method for determiningmaximum heart rate of a user and avoid above defects of prior art.

In the first embodiment the intensity model takes, in addition of heartrate, account of respiration rate and/or kinetics-information. Thatkinetics information depicts change in excess post-exercise oxygenconsumption (EPOC), more generally the direction of cumulativephysiological disturbance in homeostasis, whether it is at steady state,on-response (rising) or off-response (descending).

Generally speaking it is disclosed a method, which minimizes theabsolute value of the difference between simulated (external) intensityand physiological (internal) intensity as a function of maximal heartrate. The claimed method is able to decrease the value of the determinedmaximal heart rate, which is necessary when aiming a fully realisticresult among aging people and users in all levels.

In a useful embodiment process obtaining the value of HRmax comprisesalso calculation of its reliability inversely according to a magnitudeof the minimum value. In another embodiment the validation includesfurther steps of: calculating a weighted average floating value of allobtained values of HRmax and storing it, and comparing each calculatedaverage floating value with the initial value of HRmax and an age basedvalue of HRmax using a predetermined criteron to choose the final valueof HRmax.

In another embodiment scanning is carried in two ranges locating upwardsand downwards from a chosen starting value and the obtained value with ahigher reliability from two obtained values from said ranges is chosenas a final value of HRmax.

A BRIEF DESCRIPTION OF THE FIGURES

Advantages of embodiments of the present disclosure will be apparentfrom the following detailed description of the exemplary embodiments.The following detailed description should be considered in conjunctionwith the accompanying figures in which:

FIG. 1 presents an exemplary flowchart illustrating the main flow ofexecution of calculation of maximal heart rate (HRmax).

FIG. 2 presents the flowchart of the Estimation algorithm.

FIGS. 3 and 4 present exemplary diagrams depicting scanning theobjective function in one or two ranges.

FIGS. 5A, 5B and 5C present exemplary charts for internal-intensityfunction “int-int”—used in the estimation algorithm during three typicalexercise phases: A) steady state, B) on-response and C) off-response.

FIG. 6 presents an exemplary block diagram of a system with additionalinterfaces.

DETAILED DESCRIPTION

Aspects of the invention are disclosed in the following description andrelated drawings directed to specific embodiments of the invention.Alternate embodiments may be devised without departing from the spiritor the scope of the invention. Additionally, well-known elements ofexemplary embodiments of the invention will not be described in detailor will be omitted so as not to obscure the relevant details of theinvention. Further, to facilitate an understanding of the descriptiondiscussion of several terms used herein follows.

As used herein, the word “exemplary” means “serving as an example,instance or illustration.” The embodiments described herein are notlimiting, but rather are exemplary only. It should be understood thatthe described embodiments are not necessarily to be construed aspreferred or advantageous over other embodiments. Moreover, the terms“embodiments of the invention”, “embodiments” or “invention” do notrequire that all embodiments of the invention include the discussedfeature, advantage or mode of operation.

Further, many of the embodiments described herein are described in termsof sequences of actions to be performed by, for example, elements of acomputing device. It should be recognized by those skilled in the artthat the various sequences of actions described herein can be performedby specific circuits (e.g. application specific integrated circuits(ASICs)) and/or by program instructions executed by at least oneprocessor. Additionally, the sequence of actions described herein can beembodied entirely within any form of computer-readable storage mediumsuch that execution of the sequence of actions enables at least oneprocessor to perform the functionality described herein. Furthermore,the sequence of actions described herein can be embodied in acombination of hardware and software. Thus, the various aspects of thepresent invention may be embodied in a number of different forms, all ofwhich have been contemplated to be within the scope of the claimedsubject matter. In addition, for each of the embodiments describedherein, the corresponding form of any such embodiment may be describedherein as, for example, “a computer configured to” perform the describedaction.

The method can be implemented in versatile devices, which have resourcesfor measuring internal intensity and external workload, and run softwareto execute processes depicted in the exemplary flowcharts of FIGS. 1 and2 . A schematic hardware assembly is depicted below in exemplary FIG. 6.

Initial background and personal data may be stored. For example, thefitness level (for example VO2max or METmax) and the maximum heart rate(HRmax), and the like, of the user may be stored. Personal data may beentered or determined beforehand.

Description of the Program (FIG. 1 )

In FIG. 1 the shown calculation process of maximal heart rate (HRmax)may be a part of bigger software that monitors and analyzes physiologyof a user. External (10) and internal (12) workloads are measuredcontinuously. The external workload may be measured in many ways, forexample (but not limited to) speed, altitude, power, etc. The internalworkload is monitored usually by heart rate, which may be performed byvarious devices, such as (but not limited to) a heart rate belt, PPG- orECG-device.

External and internal workloads are monitored (5 sec interval) by a hostprocess, here embedded training effect (ETE)—library software, step 14.It will need here some background parameters, like age, sex, a possibleinitial value of HRmax, here HRmaxbg from step 15. The ETE softwarecalculates continuously several variables for HRmax estimation software.These variables are updated in every 5 seconds (more generally in 1-15seconds) and stored temporarily (runtime), step 16.

Local Variables in this Function:

The core variables metSPEED, maxMET, HR, respRate, kinetics,anMultiplier are defined below.

Several Other Parameters are Used:

w_c: reliability weight of the optimal solution (fixed point)

i: iteration index in while loop (integer)

f_old: previous value of the objective function (fixed point)

f_i: current value of the objective function (fixed point)

Int. Intensity_i: intensity (%-maxMET) at current solution (integer)

Output Arguments in this Function:

HRmax_c: optimal solution (integer)

The ETE library updates the (majority of) its global variables onceevery five seconds (more generally 1-15 seconds depending on aparticular embodiment). Before estimating the maximal heart rate, thefollowing variables are updated by ETE:

HR: current heart rate level (may be updated in principle after eachheart beat)

speed: current walking/running speed of the user

altitude: current altitude as obtained, e.g., via GPS or a barometer

power: pedaling power (only available in the case of cycling or rowing)

inclination_angle: current angle of inclination (in radians) obtaineddirectly or through sequential altitude and speed measurements

metSpeed: MET estimate calculated using external workload, calculated asVO2/3.5 (where 3.5 is a constant), where VO2 is one of following (all inunits: ml/min/kg):VO2_running=12.0*speed+3.5VO2_walking=4.3804*speed{circumflex over ( )}2−0.2996*speed+7.0928VO2_power=(12.24*power+350.0)/weight

Here, speed is in units m/s. It is the raw input speed (withoutinclination correction). The weight of the user in kilograms is given bythe variable weight.

In the case of running and walking, the inclination angle may be takeninto account as follows:

If inclination angle is positive, as when going uphill:VO2_running=VO2_running+speed*inclination_angle*54.0VO2_walking=VO2_walking+speed*inclination_angle*108.0

There are similar equations available when the inclination angle isnegative, as when going downhill.

In the case of running and walking, the software may select the minimumof these two values:VO2=MIN(VO2_running,VO2_walking),when the type of exercise is not known.

In the case of cycling, only VO2_power is available, and this value isthus assigned to the variable VO2.

maxMET: the weighted maximal MET estimate given by the ETE library.Heart rate (beat-to beat interval and/or heart rate level) and externalwork (speed and optionally altitude, or pedaling/rowing power) arerequired for estimation of maxMET. If there has not been enough reliabledata available during the current measurement (e.g., due to missingspeed data or low-quality heart rate data), the maximal MET value givenas background parameter is assigned to this variable. Otherwise thevalue of zero is used to indicate a missing value.

respRate: respiration rate (in Hz) estimated from the RRI sequence orheart rate data.

fxEpocHr: heart rate based EPOC estimate. This variable is used in theneural network that calculates the internal intensity (Int-int). Thevalue of kinetics (selection of FIGS. 5A-5C) are defined by thisvariable.

anMultiplier: multiplier (greater than or equal to one) used to increaseintensity based on anaerobic load.

floatingHRmax_ave: weighted average of maximal heart rate values givenby the HRmax estimation algorithm (FIG. 2 ) during the current trainingsession. The weights used in the calculation of the weighted average arethe reliability weights (w_c, between zero and one), as described inFIG. 2 . This variable is updated after the HRmax estimation algorithmin step 19. This variable is either initialized to zero in the beginningof the current exercise or the latest available value from the previousexercise session as the initial value.

floatingHRmax_sum_w: sum of the reliability weights (w_c) given by theHRmax estimation algorithm during the current training session. Thisvariable is updated after the HRmax estimation algorithm in step 19.This variable is either initialized to zero in the beginning of thecurrent exercise or the latest available value from the previousexercise session as the initial value.

newMaxHR: highest measured heart rate (i.e., largest value observed forthe variable HR) encountered during the current training session.

The following internal (global) variables are calculated and stored inthe initialization phase of the ETE library (in the beginning of thecurrent training session) and they remain constant throughout thecurrent training session (step 15):

age: age of the user in years

weight: weight of the user in kilograms

HRmax_age: age-based estimate for maximal heart rate: 210-0.65*age

HRmax_bg: maximal heart rate given as background parameter; if this wasnot given by the user, the default value HRmax_age is assigned to it.

The maximal heart rate estimation algorithm (step 18) is run only iffitness level estimate (i.e., non-zero value for the variable maxMET) isavailable. The maximal heart rate estimation algorithm (step 18)minimizes the absolute difference between external and internalintensities as a function of maximal heart rate. The details of thealgorithm with references to FIG. 2 are given later in the text.

After the estimation algorithm has finished with the optimal solutionHRmax_c and its reliability weight w_c as outputs, a weighted average ofHRmax solutions and the sum of reliability weights are updated with thefollowing equations (step 19):HRmax weightedaverage:floatingHRmax_ave=(floatingHRmax_ave+(w_c*HRmax_c)/floatingHRmax_sum_w)/(1+w_c/floatingHRmax_sum_w)Sum of reliability weights:floatingHRmax_sum_w=floatingMaxHR_sum_w+w_c

With these floating weight values the obtained maximum heart rateestimate (HRmax_est) is validated and stored or rejected it based on achosen criterion (step 20).

These two variables are updated after each call to the HRmax estimationroutine (step 18). Optimal solutions (HRmax_c) with higher reliabilityweights (w_c) have larger effect on the value of the weighted average(floatingHRmax_ave).

The weighted average of optimal solutions floatingHRmax_ave provides anestimate for the maximal heart rate for the user. However, in the caseof an erroneous input speed, altitude, power or heart rate data, theestimate may be unreliable. Hence, the estimate is validated (step 20)via comparing it with the values of the variables HRmax_bg, HRmax_ageand newMaxHR. Then the execution returns to handle next 5 secondsequence (to step 14). Also, the latest HRmax estimate is given asoutput every 5 seconds. Based on predetermined validation rules, thefinal maximal heart rate solution is calculated as a function of thevariables floatingHRmax_ave, floatingHRmax_sum_w, HRmax_bg, HRmax_ageand newMaxHR. The final solution and possibly a reliability calculatedfor it in the validation phase are given as output in step 23.

Description of the HRmax Estimation Algorithm (FIG. 2 )

The maximal heart rate estimation algorithm (step 18 and FIG. 2 ) isentered only if a non-zero value for maxMET is currently available.

The maximal heart rate estimation method tries to minimize the objectivefunction f=f (HR_i) as a function of maximal heart rate, i.e., thedecision variable HR_i (steps 1851 and 1852), wheref(HR_i)=|external_intensity−internal_intensity(HR_i,HR,respRate,kinetics)|

The values of the variables external_intensity, HR, respRate andkinetics remain constant during the minimization (scanning) phase ineach 5-second calculation round. Thus, only the second term internalintensity( )varies as the decision variable HR_i is changed during theoptimization (scanning) process.

The external intensity is defined as the ratio of metSpeed and maxMET:external_intensity=metSpeed/maxMET

metSpeed and maxMET are the latest available values for the variables.They remain fixed during the scanning procedure of one calculationround.

Estimate for the internal intensity (a value in the range [0,1]) isprovided by the HR based neural network model within the ETE library bythe function internal_intensity( )(abbreviation: Int_int) which is anonlinear function of the following variables:

relative heart rate: ratio between current heart rate (HR) and currentcandidate for maximal heart rate (HR_i): HR/HR_i

respRate: respiration rate (in Hz) estimated from the RRI sequence orheart rate data.

-   kinetics: difference between the current and previous (5 s earlier)    HR-based EPOC value: fxEpocHr(current)−fxEpocHr(previous)

Any model for the internal intensity should have as parameters at leastrelative heart rate (HR/HR_i) and one of said two parameters (respRate,kinetics)—preferably both parameters. An embodiment which has heart rate(HR/HR_i) as a sole parameter would also be possible but may suffer frominaccuracy.

Here, the objective function is thus the absolute value of thedifference between the external and internal intensities. It should benoted that the function can also be, for example, the second power ofthe said difference, or any other function which monotonically increasesin value as the absolute value of the difference between external andinternal intensities increase.

The estimation algorithm (FIG. 2 ) uses the latest available values ofthe global variables (13). References are also made to FIGS. 3-5C.

The host software calls this child process (step 1801) and it defines ascanning range (step 1803) and sets a step (usually 1 bpm). In thescanning process (1805) heart rate HR_i runs stepwise across the range(HRlower-HRupper) from a set starting point. In each point the value ofthe objective function is calculated (step 1851) and it is compared toprevious values (step 1852). Thus, the nth HR_i gets value fromfollowing equation: HR_n=HRlower+n×STEP.

If there is only one range (FIG. 3 ), the starting point is somewherenear the background max heart rate (HRmax_bg). If there are two ranges(FIG. 4 ) and two scanning processes, both start preferably from thebackground max heart rate (HRmax_bg) and the lowest minimum value of twominimums defines the true minimum of objective function. Finally, themaximal heart rate value which leads to the lowest value for objectivefunction is selected as HRmax candidate (variable HRmax_c in FIG. 2 ).

During scanning, the objective function f(HR_i) is calculated in eachvalue of HR_i (step 1851). The other parameters are treated as constantsduring this 5 second process and they are picked up from list of theglobal variables (13). The optimization (scanning) process can beimplemented by any numerical optimization method suitable for the task(e.g., linear scan, binary search, line search, etc.).

When the optimization of step 1852 is carried out using a linear scanusing a running variable HR_i, the scanning process is stopped once allvalues in the range (HRlower-HRupper) has been scanned at the specifiedstep size. The optimal solution (HRmax_c) is the value of the decisionvariable which produces lowest value for the objective function in thisrange. Alternatively, if the objective function may be assumed tocontain a single minimum in the scanned range, the linear scan may bestopped early as soon as the first minimum has been found. Other searchmethods such as binary search and line search become applicable whensuch assumption on unimodality of the objective function may be assumed.

The maximal heart rate HR_max_c is the only decision variable in theminimization process. It should be noted that the external_intensity isdependent on maximal heart rate, as well: the estimation of the fitnesslevel (maxMET) requires an estimate for maximal heart rate. However,based on empirical testing, the presented method converges towardscorrect solution even if value of the variable external_intensity iscalculated using the maximal heart rate value given by the backgroundvariable HRmax_bg which remains constant throughout a singlemeasurement. The presented method could be extended to the situationwhere the external_intensity depends on the running variable HR_i, i.e.on the decision variable HR_i, as well. However, it would increase thespace and time complexity of the algorithm.

The reliability of the optimal solution (HRmax_c) is quantified with anon-negative reliability weight which correlates negatively with thevalue of the objective function at the optimal solution. In one example,a reliability weight is calculated with an empiric equationw_c=1, if 0.2−5.3452×f( )>1w_c=0, if 0.2−5.3452×f( )<0w_c=0.2−5.3452×f( )otherwise.

Here, the value of the objective function at the optimal solution isdenoted with f( )

Possible anaerobic part will be checked by steps 1809, 1810 and 1811 andit will decrease the weight (w_c) of the obtained optimal solution(maximum heart rate value HRmax_c). If significant anerobic contributionis present during the current exercise, the anMultiplier variable has avalue larger than one. When anaerobic contribution is not present,anMultiplier remains at 1.0.

If anMultiplier exceeds the value of 1.0, the value of the reliabilityweight w_c is decreased, e.g., with the following empirical equation:w_c=w_c/(2×anMultiplier)(step1810).

Finally, the optimal solution (HRmax_c) and its reliability weight (w_c)have been calculated (step 1811) and they are given as outputs in step1812.

Description of the Internal Intensity Function (FIG. 5 )

There are three separate situations (FIGS. 5A-5C), each of which definesthe function used in the estimation algorithm during three typicalexercise phases: A) steady state, B) one example of on-response and C)one example of off-response. These states A, B and C are referred to as“kinetics” and they are defined by EPOC—calculation or any other methoddetermining a change in homeostasis. FIGS. 5B and 5C show just oneexample of both these states as the rate of change in fatigueaccumulation or recovery and may vary, whereas steady-state (5A) alwaysmeans a state where fatigue level remains completely unchanged.

Description of Exemplary Embodiments (FIG. 6)

The system and method according to the exemplary embodiments can beapplied in many kinds of devices as would be understood by a person ofordinary skill in the art. For example, a wrist top device with aheart-rate transmitter, a mobile device such as a phone, tablet or thelike, or other system having CPU, memory and software therein may beused.

According to exemplary FIG. 6 , in the implementation may include anassembly built around a central processing unit (CPU) 132. A bus 136 maytransmit data between the central unit 132 and the other units. Theinput unit 131, ROM memory 1311, RAM memory 1312, keypad 118, PCconnection 137, and output unit 134 may be connected to the bus. RAMmemory has an allocated area 122 for HRmax calculation, namely local andglobal variables therein.

The system may include a data logger which can be connected to cloudservice, or other storage as would be understood by a person of ordinaryskill in the art. The data logger may measure, for example,physiological response and/or external workload.

A heart rate sensor 142 and any sensor 140 registering external workloadmay be connected to the input unit 131, which may handle the sensor'sdata traffic to the bus 136. In some exemplary embodiments, the PC maybe connected to a PC connection 137. The output device, for example adisplay 145 or the like, may be connected to output unit 134. In someembodiments, voice feedback may be created with the aid of, for example,a voice synthesizer and a loudspeaker 135, instead of, or in addition tothe feedback on the display. The sensor 140 which may measure externalworkload may include any number of sensors, which may be used togetherto define the external work done by the user.

More specifically the system presented in FIG. 6 may have the followingparts for determining the maximum heart rate of a user.

-   -   a heart rate sensor (142) configured to measure the heartbeat of        the person, the heart rate signal being representative of the        heartbeat of the user;    -   at least one sensor (140) to measure an external workload during        an exercise, and    -   a data processing unit (132) operably coupled to the said        sensors (142, 140), a memory (1311, 1312) operably coupled to        the data processing unit (132),    -   the memory may be configured to save background information of a        user, for example, background data including an earlier fitness        level, as well as HRmax and the like.

The data processing unit (132) may include dedicated software configuredto execute the embodiments described in the present disclosure.

As described above in the exemplary embodiments, default values of theoptional parameters may be stored in a ROM memory, in an EEPROM(Electrically Erasable Programmable Read-Only Memory) memory, or inother memory as would be understood by a person of ordinary skill in theart.

The invention claimed is:
 1. A wearable electronic device operable to beworn by a user, the device comprising: a memory; a display; a heart ratesensor; and a processor coupled with the display, the memory, and theheart rate sensor, the processor configured to- continuously calculate aheart rate and a respiration rate for the user utilizing signalsreceived from the heart rate sensor, calculate an intensity model ofinternal intensity utilizing the heart rate and respiration rate, storethe intensity model in the memory, calculate and store a first maximumheart rate value in the memory, select a heart rate range based on thefirst maximum heart rate value, estimate a second maximum heart ratevalue based on the selected heart rate range, the intensity model, thecalculated heart rate, and the calculated respiration rate, and controlthe display to present an indication of the estimated second maximumheart rate value.
 2. The device of claim 1, wherein the processor isfurther configured to- calculate an external intensity based on valuesof external workload and maximum performance stored within the memory,and estimate the second maximum heart rate value using the calculatedexternal intensity, the selected heart rate range, the intensity model,the calculated heart rate, and the calculated respiration rate.
 3. Thedevice of claim 2, wherein the processor is further configured to-calculate a kinetics value at least a direction of cumulativephysiological disturbance in homeostasis using the calculated values ofheart rate and external workload, and estimate the second maximum heartrate value using the kinetics value, the calculated external intensity,the selected heart rate range, the intensity model, the calculated heartrate, and the calculated respiration rate.
 4. The device of claim 2,further including a sensor for measuring the external workload.
 5. Thedevice of claim 2, wherein the processor is configured to- calculate aminimal difference between internal intensity and external intensity,and estimate the second maximum heart rate utilizing at least thecalculated minimal difference.
 6. The device of claim 1, wherein theintensity model includes three input states: A—recovery, B—steady-stateand C—accumulating fatigue selected by determining a change inhomeostasis.
 7. A wearable electronic device operable to be worn by auser, the device comprising: a memory; a display; a workload sensor; aheart rate sensor; and a processor coupled with the display, the memory,and the heart rate sensor, the processor configured to- continuouslycalculate a heart rate and a respiration rate for the user utilizingsignals received from the heart rate sensor, calculate an intensitymodel of internal intensity utilizing the heart rate and respirationrate, calculate an external intensity based on values of externalworkload received from the workload sensor; store the intensity model inthe memory, calculate and store a first maximum heart rate value in thememory, select a heart rate range based on the first maximum heart ratevalue, estimate a second maximum heart rate value based on the selectedheart rate range, the intensity model, the external intensity, thecalculated heart rate, and the calculated respiration rate, and controlthe display to present an indication of the estimated second maximumheart rate value.
 8. The device of claim 7, wherein the processor isfurther configured to- calculate a kinetics value at least a directionof cumulative physiological disturbance in homeostasis using thecalculated values of heart rate and external workload, and estimate thesecond maximum heart rate value using the kinetics value, the calculatedexternal intensity, the selected heart rate range, the intensity model,the calculated heart rate, and the calculated respiration rate.
 9. Thedevice of claim 7, wherein the processor is configured to- calculate aminimal difference between internal intensity and external intensity,and estimate the second maximum heart rate utilizing at least thecalculated minimal difference.
 10. The device of claim 7, wherein theintensity model includes three input states: A—recovery, B—steady-stateand C—accumulating fatigue selected by determining a change inhomeostasis.